Conceptual challenges for interpretable machine learning.
As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as inte...
| Publicado en: | Synthese Vol. 200; no. 1; pp. 1 - 17 |
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| Formato: | Artículo |
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Springer Nature
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |